CN116992243B - AIOT-based industrial solid waste treatment material management method and system - Google Patents
AIOT-based industrial solid waste treatment material management method and system Download PDFInfo
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Abstract
The invention discloses an AIOT-based industrial solid waste treatment material management method and system, which relate to the technical field of industrial solid waste treatment material management and comprise an information acquisition module, a server, a comparison module, a comprehensive analysis module and a prompt module; the information acquisition module acquires a plurality of data information including physical property data information and communication property data information when the gas detector in the AIOT-based industrial solid waste treatment material management system operates. According to the invention, the operation state of the gas detector in the AIOT-based industrial solid waste treatment material management system is monitored, when the detection precision and the detection accuracy of the gas detector are abnormal, the system is used for sensing and prompting related staff to evacuate, so that the situation that dangerous gases such as combustible gases and toxic gases generated by chemical reaction equipment are not detected in time is effectively prevented, and the life safety of the related staff is ensured.
Description
Technical Field
The invention relates to the technical field of industrial solid waste treatment material management, in particular to an AIOT-based industrial solid waste treatment material management method and system.
Background
The AIOT (Internet of things and artificial intelligence) -based industrial solid waste treatment material management refers to the monitoring, tracking, management and optimization of materials in the industrial solid waste treatment process by utilizing the Internet of things technology and the artificial intelligence technology. The method can improve the efficiency, safety and sustainability of solid waste treatment.
The gas detector in the AIOT-based industrial solid waste treatment material management system can be used for monitoring chemical reaction equipment because various gases including combustible gases, toxic gases, steam and the like are often generated in the chemical reaction process, and the generation and the discharge of the gases can threaten the health and the safety of operators and even can cause dangers such as fire, explosion and the like.
For example, gas in chemical reaction may leak due to equipment failure, misoperation and the like, and the gas detector can monitor the gas concentration in time to find leakage problem, so that appropriate emergency measures are taken; some chemical reactions can generate toxic gases, such as ammonia gas, hydrogen sulfide and the like, and the gas detector can detect the concentration of the toxic gases and timely give an alarm so as to ensure that operators are not exposed to the environment of the toxic gases; the chemical reaction may produce combustible gases such as methane, ethane, etc., and the gas detector may detect the concentration of the combustible gases, preventing the risk of fire and explosion.
The prior art has the following defects: however, when the accuracy of gas detection is reduced due to the reduction of the detection accuracy of the gas detector in the AIOT-based industrial solid waste treatment material management system, the system cannot sense, and if dangerous gases such as combustible gases and toxic gases generated by chemical reaction equipment are not detected in time, the life safety of related staff is threatened greatly.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide an AIOT-based industrial solid waste treatment material management method and system, wherein the AIOT-based industrial solid waste treatment material management system monitors the operation state of a gas detector, senses and prompts related staff to evacuate through the system when the detection precision and the detection accuracy of the gas detector are abnormal, so that the situation that dangerous gases such as combustible gases and toxic gases generated by chemical reaction equipment are not detected in time is effectively prevented, and the life safety of the related staff is ensured, thereby solving the problems in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: the AIOT-based industrial solid waste treatment material management system comprises an information acquisition module, a server, a comparison module, a comprehensive analysis module and a prompt module;
the information acquisition module acquires a plurality of data information including physical property data information and communication property data information when the gas detector operates in the AIOT-based industrial solid waste treatment material management system, processes the physical property data information and the communication property data information when the gas detector operates and transmits the processed physical property data information and communication property data information to the server;
the server comprehensively analyzes the processed physical property data information and communication property data information when the gas detector operates, generates an accuracy index, and transmits the accuracy index to the comparison module;
the comparison module is used for comparing and analyzing the precision index generated by the gas detector during operation with a preset precision index reference threshold value to generate a high hidden danger signal or a low hidden danger signal, and transmitting the high hidden danger signal to the comprehensive analysis module;
the comprehensive analysis module is used for establishing an analysis set for comprehensive analysis on the precision index generated during subsequent operation of the gas detector after the high hidden danger signal generated during the operation of the gas detector is obtained, generating an early warning signal, transmitting the early warning signal to the prompt module, and sending different early warning prompts through the prompt module.
Preferably, the physical performance data information of the gas detector during operation comprises a linearity anomaly hiding coefficient and a measurement result variation coefficient, the communication performance data information of the gas detector during operation comprises a data transmission frequency drift coefficient, and after acquisition, the information acquisition module respectively marks the linearity anomaly hiding coefficient and the measurement result variation coefficient asAnd->Calibrating the data transmission frequency drift coefficient to be +.>。
Preferably, the logic for obtaining the linearity anomaly concealment coefficients is as follows:
a101, acquiring an initial linearity value of a gas detector in an AIOT-based industrial solid waste treatment material management system, and calibrating the initial linearity value as；
A102, acquiring actual output signal values at different moments in T time and actual gas concentration values under corresponding output signals in the operation process of the gas detector, calculating an actual linearity value according to the ratio of the actual output signal values to the actual gas concentration values under the corresponding output signals, and calibrating the actual linearity value as，xA number representing the actual linearity values at different times during operation of the gas detector during time T,x=1、2、3、4、……、m，mis a positive integer;
a103, through initial linearity valueAnd the actual linearity value +.>And calculating a linearity abnormal hiding coefficient, wherein the calculated expression is as follows: />。
Preferably, the logic for obtaining the coefficient of variation of the measurement results is as follows:
b101, acquiring actual measurement results of a gas detector in an AIOT-based industrial solid waste treatment material management system at different moments in T time, and combining the actual measurement resultsThe fruit is marked as，yThe number representing the actual measurement results of the gas detector at different times during the time T,y=1、2、3、4、……、n，nis a positive integer;
b102, calculating an actual measurement result standard deviation through an actual measurement result obtained by the gas detector in the T time, and calibrating the actual measurement result standard deviation asQStandard deviation of actual measurement resultQThe calculation formula of (2) is as follows:wherein->For an actual measurement result average value calculated by an actual measurement result acquired by the gas detector in the T time, the calculated expression is: />；
B103 by actual measurement result standard deviationQAnd the average value of the actual measurement resultsCalculating a variation coefficient of a measurement result, wherein the calculated expression is as follows: />。
Preferably, the logic for obtaining the data transmission frequency drift coefficient is as follows:
c101, acquiring an optimal data transmission frequency range between a gas detector and a central management system in an AIOT-based industrial solid waste treatment material management system, and calibrating the optimal data transmission frequency range as；
C102, acquiring information of data transmission between the central management system and the T time in the operation process of the gas detector, and carrying out data transmission on the informationThe actual data transmission frequency of two adjacent data transmissions is calibrated as，kA number indicating the actual data transmission frequency at the time of two adjacent data transmissions between the gas detector and the central management system,k=1、2、3、4、……、p，pis a positive integer;
c103, transmitting frequency of actual data acquired in T time in operation process of gas detectorAnd the optimal data transmission frequency range->The comparison is carried out and the maximum value of the frequency range of the data transmission is greater than + ->Is recalibrated to +.>，vRepresenting a maximum value +.>Is a number of actual data transmission frequencies of (a),v=1、2、3、4、……、q，qis a positive integer;
c104 maximum value of frequency range through optimal data transmissionAnd the actual data transmission frequency->Calculating a data transmission frequency drift coefficient, wherein the calculated expression is as follows: />In which, in the process,pis the total amount of actual data transmission frequency acquired in the time T during the operation of the gas detector.
Preferably, the server obtains the linearity anomaly concealment coefficientsCoefficient of variation of measurement results->Data transmission frequency drift coefficient +.>Then, a data processing model is built, and an accuracy index is generated>The formula according to is:wherein->、/>、/>Respectively linearity anomaly concealment coefficient +.>Coefficient of variation of measurement results->Data transmission frequency drift coefficient->Is a preset proportionality coefficient of (1), and、/>、/>all are bigAt 0.
Preferably, the comparison module compares and analyzes the precision index generated when the gas detector operates with a preset precision index reference threshold, if the precision index is larger than or equal to the precision index reference threshold, a high hidden danger signal is generated through the comparison module and transmitted to the prompt module, an early warning prompt is sent out through the prompt module, if the precision index is smaller than the precision index reference threshold, a low hidden danger signal is generated through the comparison module and transmitted to the prompt module, and the early warning prompt is not sent out through the prompt module.
Preferably, the comprehensive analysis module establishes an analysis set for an accuracy index generated during subsequent operation of the gas detector after acquiring a high hidden trouble signal generated during operation of the gas detector, and calibrates the analysis set asIThen，fA number representing the precision index within the analysis set,f=1、2、3、4、……、u，uis a positive integer;
calculating a standard deviation of the precision index and a mean value of the precision index through the precision index in the analysis set, and respectively comparing the standard deviation of the precision index and the mean value of the precision index with a preset standard deviation reference threshold value and a preset precision index reference threshold value to obtain the following comparison analysis results:
if the average value of the precision indexes is larger than or equal to the reference threshold value of the precision indexes, a first-level early warning signal is generated, the signal is transmitted to a prompt module, and a first-level early warning prompt is sent out through the prompt module;
if the precision index average value is smaller than the precision index reference threshold value and the precision index standard deviation is larger than or equal to the standard deviation reference threshold value, generating a secondary early warning signal, transmitting the signal to a prompt module, and sending out a secondary early warning prompt through the prompt module;
if the precision index average value is smaller than the precision index reference threshold value and the precision index standard deviation is smaller than the standard deviation reference threshold value, generating a three-level early warning signal, transmitting the signal to the prompt module, and sending out early warning prompt without the prompt module.
The AIOT-based industrial solid waste treatment material management method comprises the following steps:
acquiring a plurality of data information including physical property data information and communication property data information when a gas detector operates in an AIOT-based industrial solid waste treatment material management system, and processing the physical property data information and the communication property data information when the gas detector operates after acquisition;
comprehensively analyzing the processed physical property data information and communication property data information of the gas detector during operation to generate an accuracy index;
comparing and analyzing the precision index generated by the gas detector during operation with a preset precision index reference threshold value to generate a high hidden danger signal or a low hidden danger signal;
after the high hidden danger signal generated during the operation of the gas detector is obtained, the analysis set is established for the precision index generated during the subsequent operation of the gas detector for comprehensive analysis, the early warning signal is generated, and different early warning prompts are sent out to the early warning signal.
In the technical scheme, the invention has the technical effects and advantages that:
according to the invention, the operation state of the gas detector in the AIOT-based industrial solid waste treatment material management system is monitored, when the detection precision and the detection accuracy of the gas detector are abnormal, the system is used for sensing and prompting related staff to evacuate, so that the situation that dangerous gases such as combustible gases and toxic gases generated by chemical reaction equipment are not detected in time is effectively prevented, and the life safety of the related staff is ensured;
when the condition that the gas detector possibly has abnormal running state is sensed, the running state of the gas detector is judged by comprehensively analyzing the precision index generated during the running of the gas detector, firstly, the abnormal condition of the gas detector can be classified, secondly, the possibility of accidental abnormal triggering early warning prompt can be effectively prevented, the gas detector can be ensured to run stably and efficiently, and further, the industrial solid waste treatment material management system based on AIOT can be ensured to run stably and efficiently.
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For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present invention, and that other drawings may be obtained according to these drawings for a person skilled in the art.
FIG. 1 is a schematic block diagram of the AIOT-based industrial solid waste treatment material management method and system of the present invention.
FIG. 2 is a flow chart of a method for AIOT-based industrial solid waste treatment material management method and system of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The invention provides an AIOT-based industrial solid waste treatment material management system shown in figure 1, which comprises an information acquisition module, a server, a comparison module, a comprehensive analysis module and a prompt module;
the information acquisition module acquires a plurality of data information including physical property data information and communication property data information when the gas detector operates in the AIOT-based industrial solid waste treatment material management system, processes the physical property data information and the communication property data information when the gas detector operates and transmits the processed physical property data information and communication property data information to the server;
the physical property data information of the gas detector during operation comprises a linearity anomaly hiding coefficient and a measurement result variation coefficient, and after acquisition, the linearity anomaly hiding coefficient and the measurement result variation coefficient are respectively calibrated asAnd->；
The linearity of the gas detector means that the relation between the output signal of the gas detector and the concentration of the gas to be detected can be kept linear within a certain range, in other words, when the concentration of the gas changes, the output signal of the gas detector should change in the same proportion and accord with the linear relation;
when the linearity deviation of the gas detector in the AIOT-based industrial solid waste treatment material management system is large, the detection accuracy of the gas detector is reduced, and the accuracy of gas detection is further reduced, for the following reasons:
nonlinear response: when the linearity deviation of the gas detector is large, the relation between the response and the gas concentration may become no longer linear, which means that when the gas concentration changes, the output signal of the detector may not change according to the expected proportion, but deviate, and the nonlinear response may cause the difference between the actual measured value and the actual gas concentration to increase, so that the detection accuracy is reduced;
error accumulation: the nonlinear response may cause the output signal of the gas detector in different concentration ranges to deviate from the actual value, and such deviation may gradually accumulate in different concentration ranges, which means that even if the measurement error of the detector is small at a certain concentration, when the concentration changes, the error gradually increases, resulting in a decrease in the detection accuracy;
the data is unreliable: the nonlinear response may cause an unstable deviation between the measured value output by the detector and the actual gas concentration, which makes the data acquired from the detector unreliable, which may lead to erroneous decisions, erroneous decisions and erroneous understanding of the gas concentration;
therefore, the output signal and the concentration of the gas to be detected are monitored when the gas detector operates, and the problem that the linearity of the gas detector is abnormal can be found in time;
the logic for linearity anomaly concealment coefficient acquisition is as follows:
a101, acquiring an initial linearity value of a gas detector in an AIOT-based industrial solid waste treatment material management system, and calibrating the initial linearity value as；
It should be noted that, the manufacturer of the gas detector will typically provide relevant performance parameter data, including linearity, which is given in a product specification table, a user manual or a technical document, and which is typically obtained by testing with standard gas in a laboratory inside the manufacturer;
a102, acquiring actual output signal values at different moments in T time and actual gas concentration values under corresponding output signals in the operation process of the gas detector, calculating an actual linearity value according to the ratio of the actual output signal values to the actual gas concentration values under the corresponding output signals, and calibrating the actual linearity value as，xA number representing the actual linearity values at different times during operation of the gas detector during time T,x=1、2、3、4、……、m，mis a positive integer;
it should be noted that, the gas detector generally uses a gas sensor to measure the concentration of the target gas, the gas sensor will respond to the target gas, the physical quantity of the response may be resistance, voltage, current, etc., and the measured value of the gas detector can be obtained by reading the output signal of the sensor; the gas detector outputs analog signals, usually voltages or currents, the amplitude of which is proportional to the concentration of the gas to be detected, an analog-to-digital converter (ADC) can be used to convert the analog signals into digital signals, and then a microcontroller or an embedded system is used to acquire digital signal values;
a103, through initial linearity valueAnd the actual linearity value +.>And calculating a linearity abnormal hiding coefficient, wherein the calculated expression is as follows: />；
The calculation expression of the linearity anomaly hiding coefficient shows that the larger the expression value of the linearity anomaly hiding coefficient generated when the gas detector operates in the T time in the AIOT-based industrial solid waste treatment material management system is, the lower the detection precision of the gas detector is, the lower the accuracy of the gas detector on gas detection is, and otherwise, the higher the detection precision of the gas detector is, the higher the accuracy of the gas detector on gas detection is;
in an AIOT-based industrial solid waste treatment material management system, a measurement result variation coefficient of a gas detector is a statistical index for measuring the variation degree of measurement data, the measurement result variation coefficient represents the ratio of standard deviation to average value, and is usually represented in a percentage form, and the higher the variation coefficient is, the greater the variation degree of the measurement data is, and the higher the dispersion degree of the data is;
when the variation coefficient of the measurement result of the gas detector in the AIOT-based industrial solid waste treatment material management system is high, the detection precision of the gas detector is reduced, so that the accuracy of gas detection is reduced, and the following reasons are that:
uncertainty increases: a higher coefficient of variation of the measurement results means a greater degree of dispersion of the measurement values, and a more pronounced difference between the data, which leads to an increased uncertainty in the measurement, since it is difficult to determine which value represents the actual measurement result;
error accumulation: if the variation of the measurement result is high, even if the measurement is performed under the same condition for a plurality of times, the value obtained by each measurement may have a large difference, which may cause the error to gradually accumulate, so that the accuracy of the measurement result gradually decreases;
the change is difficult to judge: in applications requiring monitoring of gas concentration changes, a higher measurement result variation coefficient makes it difficult for the detector to accurately determine the trend of the gas concentration changes, because the fluctuation of the measurement value is larger, which may lead to difficulty in distinguishing the difference between the actual concentration change and the variation of the instrument itself;
misjudgment and false decision: high measurement variation coefficients may lead to erroneous decisions, i.e. fluctuations caused by instrument variations are mistaken for a real change of gas concentration, which may lead to erroneous decisions, affecting the operation and management of the system;
affecting safety: in the fields of safety and environmental protection, such as industrial solid waste treatment material management systems, accurate gas concentration monitoring is of vital importance, and the high measurement result variation coefficient may cause that the actual change of the gas concentration cannot be found in time, thereby affecting the timely implementation of safety measures;
therefore, the problem that the variation coefficient of the measurement result of the gas detector is high can be found out in time by monitoring the variation coefficient of the measurement result of the gas detector during operation;
the measurement result variation coefficient is obtained by the following logic:
b101, acquiring actual measurement results (actually measured gas concentration values) of a gas detector in an AIOT-based industrial solid waste treatment material management system at different moments in T time, and calibrating the actual measurement results as，yThe number representing the actual measurement results of the gas detector at different times during the time T,y=1、2、3、4、……、n，nis a positive integer;
it should be noted that, the gas detector generally has various communication interfaces, such as Ethernet, wi-Fi, bluetooth, etc., which can be used to transmit measurement data to the system, and through these interfaces, real-time data transmission can be realized for real-time monitoring and analysis;
b102, calculating an actual measurement result standard deviation through an actual measurement result obtained by the gas detector in the T time, and calibrating the actual measurement result standard deviation asQStandard deviation of actual measurement resultQThe calculation formula of (2) is as follows:wherein->For an actual measurement result average value calculated by an actual measurement result acquired by the gas detector in the T time, the calculated expression is: />；
B103 by actual measurement result standard deviationQAnd the average value of the actual measurement resultsCalculating a variation coefficient of a measurement result, wherein the calculated expression is as follows: />；
The calculation expression of the variation coefficient of the measurement result shows that the larger the expression value of the variation coefficient of the measurement result generated when the gas detector operates in the T time in the industrial solid waste treatment material management system based on AIOT is, the lower the detection precision of the gas detector is, the lower the accuracy of the gas detector on gas detection is, and otherwise, the higher the detection precision of the gas detector is, the higher the accuracy of the gas detector on gas detection is;
the communication performance data information of the gas detector during operation comprises a data transmission frequency drift coefficient, and after acquisition, the data transmission frequency drift coefficient is calibrated as；
In the AIOT-based industrial solid waste treatment material management system, the data transmission frequency between the gas detector and the central management system refers to the frequency at which the gas detector transmits collected gas concentration data to the central management system, in other words, the time interval for transmitting the data from the gas detector to the central management system;
abnormal data transmission frequency may cause the detection accuracy of a gas detector in an AIOT-based industrial solid waste treatment material management system to be reduced, thereby reducing the accuracy of gas detection, because abnormal transmission frequency may affect the data acquisition and transmission process, thereby affecting the data analysis and monitoring, resulting in the following problems:
data hysteresis: when the data transmission frequency is too low or interrupted, the central management system may not be able to obtain the latest gas concentration data in time, which may lead to a response lag of the system to the real-time gas concentration change, affecting the real-time performance and fault monitoring capability of the system;
misjudgment: if the data transmission frequency is abnormal, the gas detector may transmit wrong or outdated data at a critical moment, which may cause the central management system to misunderstand that the gas concentration is normal, and in fact, an abnormal situation occurs, so that proper measures cannot be taken in time;
incomplete data: abnormal data transmission frequency may cause partial data packets to be lost or not transmitted, which may cause the central management system to receive incomplete data, which may cause inaccurate data analysis and cause misjudgment on gas concentration;
the long-term trend cannot be monitored: transmission frequency anomalies may prevent the central management system from obtaining enough data to analyze and identify long-term trends in gas concentration, which are critical to prediction and maintenance;
the system reaction is insensitive: when the transmission frequency is abnormal, resulting in data delay or instability, the central management system may not be able to quickly detect an emergency or abnormal situation, which may make the system's response insensitive;
therefore, the problem of the data transmission frequency between the gas detector and the central management system can be found in time by monitoring the data transmission frequency between the gas detector and the central management system when the gas detector is in operation;
the logic for obtaining the data transmission frequency drift coefficient is as follows:
c101, acquiring gas detector and center in AIOT-based industrial solid waste treatment material management systemManaging and calibrating optimal data transmission frequency ranges between systems；
It should be noted that, the optimal data transmission frequency range between the gas detector and the central management system needs to consider the actual requirement of the application, if the application needs to monitor the change of the gas concentration in real time, the transmission frequency may need to be higher, if the application does not have high requirement on real time, the transmission frequency may be properly adjusted to reduce the resource consumption, the setting of the optimal data transmission frequency range between the gas detector and the central management system is not particularly limited herein, and the above-mentioned optimal data transmission frequency range, that is, the optimal time interval range between two adjacent data transmissions between the gas detector and the central management system;
c102, acquiring information of data transmission between the gas detector and the central management system in the T time in the operation process of the gas detector, and calibrating actual data transmission frequency of two adjacent data transmission to be the same as，kA number indicating the actual data transmission frequency at the time of two adjacent data transmissions between the gas detector and the central management system,k=1、2、3、4、……、p，pis a positive integer;
it should be noted that, the central management system may record the transmission activity of the gas detector, including a transmission time stamp, and may obtain the information of the data transmission frequency by analyzing the log record, where the actual data transmission frequency of the two adjacent data transmissions is the actual data transmission time interval of the two adjacent data transmissions;
c103, transmitting frequency of actual data acquired in T time in operation process of gas detectorAnd the optimal data transmission frequency range->The comparison is carried out and the maximum value of the frequency range of the data transmission is greater than + ->Is recalibrated to +.>，vRepresenting a maximum value +.>Is a number of actual data transmission frequencies of (a),v=1、2、3、4、……、q，qis a positive integer;
c104 maximum value of frequency range through optimal data transmissionAnd the actual data transmission frequency->Calculating a data transmission frequency drift coefficient, wherein the calculated expression is as follows: />In which, in the process,pthe total amount of actual data transmission frequency acquired in the time T in the running process of the gas detector;
the calculation expression of the data transmission frequency drift coefficient shows that the larger the expression value of the data transmission frequency drift coefficient generated when the gas detector operates in the T time in the industrial solid waste treatment material management system based on AIOT is, the lower the detection precision of the gas detector is, the lower the accuracy of the gas detector on gas detection is, and otherwise, the higher the detection precision of the gas detector is, the higher the accuracy of the gas detector on gas detection is;
the server comprehensively analyzes the processed physical property data information and communication property data information when the gas detector operates, generates an accuracy index, and transmits the accuracy index to the comparison module;
the server obtains the linearity anomaly hiding coefficientCoefficient of variation of measurement results->Data transmission frequency drift coefficient +.>Then, a data processing model is built, and an accuracy index is generated>The formula according to is:wherein->、/>、/>Respectively linearity anomaly concealment coefficient +.>Coefficient of variation of measurement results->Data transmission frequency drift coefficient->Is a preset proportionality coefficient of (1), and、/>、/>are all greater than 0;
as can be seen from the calculation formula, the greater the linearity abnormal hiding coefficient generated when the gas detector operates in the T time, the greater the variation coefficient of the measurement result and the greater the data transmission frequency drift coefficient in the AIOT-based industrial solid waste treatment material management system, namely the accuracy index generated when the gas detector operates in the T timeThe larger the expression value of the gas detector is, the lower the detection precision of the gas detector is, the lower the accuracy of the gas detector on gas detection is, otherwise, the higher the detection precision of the gas detector is, and the higher the accuracy of the gas detector on gas detection is;
the comparison module is used for comparing and analyzing the precision index generated by the gas detector during operation with a preset precision index reference threshold value to generate a high hidden danger signal or a low hidden danger signal, and transmitting the high hidden danger signal to the comprehensive analysis module;
the comparison module is used for comparing and analyzing the precision index generated by the gas detector during operation with a preset precision index reference threshold, if the precision index is larger than or equal to the precision index reference threshold, a high hidden danger signal is generated through the comparison module and transmitted to the prompting module, an early warning prompt is sent out through the prompting module, when the gas detector generates the high hidden danger signal during operation, the detection precision of the gas detector is lower, if the precision index is smaller than the precision index reference threshold, a low hidden danger signal is generated through the comparison module and transmitted to the prompting module, and an early warning prompt is not sent out through the prompting module, when the gas detector generates the low hidden danger signal during operation, the detection precision of the gas detector is higher, and the detection precision of the gas detector is higher;
the comprehensive analysis module is used for establishing an analysis set for comprehensively analyzing the precision indexes generated during the subsequent operation of the gas detector after the high hidden danger signals generated during the operation of the gas detector are obtained, generating early warning signals, transmitting the early warning signals to the prompt module, and sending different early warning prompts through the prompt module;
the comprehensive analysis module is used for establishing an analysis set for the precision index generated during the subsequent operation of the gas detector after acquiring the high hidden danger signal generated during the operation of the gas detector, and calibrating the analysis set asIThen，fA number representing the precision index within the analysis set,f=1、2、3、4、……、u，uis a positive integer;
calculating a standard deviation of the precision index (a calculation formula of the standard deviation is analogous to a calculation formula of the standard deviation of the actual measurement result, which is not described in detail herein) and a mean value of the precision index by analyzing the precision index in the collection, and comparing the standard deviation of the precision index and the mean value of the precision index with a preset standard deviation reference threshold value and a preset precision index reference threshold value respectively for analysis, wherein the comparison analysis results are as follows:
if the average value of the precision indexes is larger than or equal to the reference threshold value of the precision indexes, a first-level early warning signal is generated and transmitted to a prompt module, the prompt module sends out a first-level early warning prompt, and when the first-level early warning prompt is generated when the gas detector operates, the gas detector is indicated to have a high probability of abnormality, and an maintainer needs to be informed to check and overhaul in time;
if the average value of the precision indexes is smaller than the reference threshold value of the precision indexes and the standard deviation of the precision indexes is larger than or equal to the reference threshold value of the standard deviation, generating a secondary early warning signal, transmitting the signal to a prompt module, and sending a secondary early warning prompt through the prompt module, wherein when the secondary early warning prompt is generated when the gas detector operates, the gas detector is poor in operation stability, and a maintainer is required to be informed to check and overhaul in time;
if the average value of the precision indexes is smaller than the reference threshold value of the precision indexes and the standard deviation of the precision indexes is smaller than the reference threshold value of the standard deviation, generating three-level early warning signals and transmitting the signals to a prompt module, and sending early warning prompts without the prompt module, when the three-level early warning prompts are generated during the operation of the gas detector, the gas detector is indicated to operate better, occasional abnormality can occur, and at the moment, the maintenance personnel are not required to be informed to check and overhaul in time;
according to the invention, the operation state of the gas detector in the AIOT-based industrial solid waste treatment material management system is monitored, when the detection precision and the detection accuracy of the gas detector are abnormal, the system is used for sensing and prompting related staff to evacuate, so that the situation that dangerous gases such as combustible gases and toxic gases generated by chemical reaction equipment are not detected in time is effectively prevented, and the life safety of the related staff is ensured;
when the condition that the gas detector possibly has abnormal running state is sensed, the running state of the gas detector is judged by comprehensively analyzing the precision index generated when the gas detector runs, firstly, the abnormal condition of the gas detector can be classified, maintenance personnel can be facilitated, secondly, the possibility of accidental abnormal triggering early warning prompt can be effectively prevented, the gas detector can be ensured to run stably and efficiently, and further, the industrial solid waste treatment material management system based on AIOT can be ensured to run stably and efficiently.
The invention provides an AIOT-based industrial solid waste treatment material management method as shown in figure 2, which comprises the following steps:
acquiring a plurality of data information including physical property data information and communication property data information when a gas detector operates in an AIOT-based industrial solid waste treatment material management system, and processing the physical property data information and the communication property data information when the gas detector operates after acquisition;
comprehensively analyzing the processed physical property data information and communication property data information of the gas detector during operation to generate an accuracy index;
comparing and analyzing the precision index generated by the gas detector during operation with a preset precision index reference threshold value to generate a high hidden danger signal or a low hidden danger signal;
after a high hidden trouble signal generated during the operation of the gas detector is obtained, an analysis set is established for the precision index generated during the subsequent operation of the gas detector for comprehensive analysis, an early warning signal is generated, and different early warning prompts are sent out to the early warning signal;
the method for managing the industrial solid waste treatment material based on the AIOT is realized through the system for managing the industrial solid waste treatment material based on the AIOT, and the specific method and the flow of the method for managing the industrial solid waste treatment material based on the AIOT are detailed in the embodiment of the system for managing the industrial solid waste treatment material based on the AIOT, and are not repeated herein.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. The AIOT-based industrial solid waste treatment material management system is characterized by comprising an information acquisition module, a server, a comparison module, a comprehensive analysis module and a prompt module;
the information acquisition module acquires a plurality of data information including physical property data information and communication property data information when the gas detector operates in the AIOT-based industrial solid waste treatment material management system, processes the physical property data information and the communication property data information when the gas detector operates and transmits the processed physical property data information and communication property data information to the server;
the physical performance data information of the gas detector during operation comprises a linearity abnormal hiding coefficient and a measuring result variation coefficient, the communication performance data information of the gas detector during operation comprises a data transmission frequency drift coefficient, and after acquisition, the information is acquiredThe linearity anomaly hiding coefficient and the measurement result variation coefficient are respectively calibrated as by the information acquisition moduleAnd->Calibrating the data transmission frequency drift coefficient to be +.>；
The logic for linearity anomaly concealment coefficient acquisition is as follows:
a101, acquiring an initial linearity value of a gas detector in an AIOT-based industrial solid waste treatment material management system, and calibrating the initial linearity value as；
A102, acquiring actual output signal values at different moments in T time and actual gas concentration values under corresponding output signals in the operation process of the gas detector, calculating an actual linearity value according to the ratio of the actual output signal values to the actual gas concentration values under the corresponding output signals, and calibrating the actual linearity value as，xA number representing the actual linearity values at different times during operation of the gas detector during time T,x=1、2、3、4、……、m，mis a positive integer;
a103, through initial linearity valueAnd the actual linearity value +.>And calculating a linearity abnormal hiding coefficient, wherein the calculated expression is as follows: />；
The measurement result variation coefficient is obtained by the following logic:
b101, acquiring actual measurement results of a gas detector in an AIOT-based industrial solid waste treatment material management system at different moments in T time, and calibrating the actual measurement results as，yThe number representing the actual measurement results of the gas detector at different times during the time T,y=1、2、3、4、……、n，nis a positive integer;
b102, calculating an actual measurement result standard deviation through an actual measurement result obtained by the gas detector in the T time, and calibrating the actual measurement result standard deviation asQStandard deviation of actual measurement resultQThe calculation formula of (2) is as follows:wherein->For an actual measurement result average value calculated by an actual measurement result acquired by the gas detector in the T time, the calculated expression is: />；
B103 by actual measurement result standard deviationQAnd the average value of the actual measurement resultsCalculating a variation coefficient of a measurement result, wherein the calculated expression is as follows: />；
The logic for obtaining the data transmission frequency drift coefficient is as follows:
c101, obtaining gas in AIOT-based industrial solid waste treatment material management systemThe optimal data transmission frequency range between the body detector and the central management system is calibrated as；
C102, acquiring information of data transmission between the gas detector and the central management system in the T time in the operation process of the gas detector, and calibrating actual data transmission frequency of two adjacent data transmission to be the same as，kA number indicating the actual data transmission frequency at the time of two adjacent data transmissions between the gas detector and the central management system,k=1、2、3、4、……、p，pis a positive integer;
c103, transmitting frequency of actual data acquired in T time in operation process of gas detectorAnd the optimal data transmission frequency range->The comparison is carried out and the maximum value of the frequency range of the data transmission is greater than + ->Is recalibrated to +.>，vRepresenting a maximum value +.>Is a number of actual data transmission frequencies of (a),v=1、2、3、4、……、q，qis a positive integer;
c104 maximum value of frequency range through optimal data transmissionAnd the actual data transmission frequency->Calculating a data transmission frequency drift coefficient, wherein the calculated expression is as follows: />In which, in the process,pthe total amount of actual data transmission frequency acquired in the time T in the running process of the gas detector;
the server comprehensively analyzes the processed physical property data information and communication property data information when the gas detector operates, generates an accuracy index, and transmits the accuracy index to the comparison module;
the server obtains the linearity anomaly hiding coefficientCoefficient of variation of measurement results->Data transmission frequency drift coefficient +.>Then, a data processing model is built, and an accuracy index is generated>The formula according to is:wherein->、/>、/>Respectively linearity anomaly concealment coefficient +.>Coefficient of variation of measurement results->Data transmission frequency drift coefficient->Is a preset proportionality coefficient of (1), and、/>、/>are all greater than 0;
the comparison module is used for comparing and analyzing the precision index generated by the gas detector during operation with a preset precision index reference threshold value to generate a high hidden danger signal or a low hidden danger signal, and transmitting the high hidden danger signal to the comprehensive analysis module;
the comprehensive analysis module is used for establishing an analysis set for comprehensive analysis on the precision index generated during subsequent operation of the gas detector after the high hidden danger signal generated during the operation of the gas detector is obtained, generating an early warning signal, transmitting the early warning signal to the prompt module, and sending different early warning prompts through the prompt module.
2. The AIOT-based industrial solid waste treatment material management system according to claim 1, wherein the comparison module compares the precision index generated when the gas detector operates with a preset precision index reference threshold value, generates a high hidden danger signal through the comparison module if the precision index is greater than or equal to the precision index reference threshold value, transmits the signal to the prompt module, sends out an early warning prompt through the prompt module, generates a low hidden danger signal through the comparison module if the precision index is less than the precision index reference threshold value, and transmits the signal to the prompt module without sending out the early warning prompt through the prompt module.
3. The AIOT-based industrial solid waste treatment material management system according to claim 2, wherein the comprehensive analysis module, after acquiring a high hidden trouble signal generated during operation of the gas detector, establishes an analysis set for an accuracy index generated during subsequent operation of the gas detector, and calibrates the analysis set asIThen，fA number representing the precision index within the analysis set,f=1、2、3、4、……、u，uis a positive integer;
calculating a standard deviation of the precision index and a mean value of the precision index through the precision index in the analysis set, and respectively comparing the standard deviation of the precision index and the mean value of the precision index with a preset standard deviation reference threshold value and a preset precision index reference threshold value to obtain the following comparison analysis results:
if the average value of the precision indexes is larger than or equal to the reference threshold value of the precision indexes, a first-level early warning signal is generated, the signal is transmitted to a prompt module, and a first-level early warning prompt is sent out through the prompt module;
if the precision index average value is smaller than the precision index reference threshold value and the precision index standard deviation is larger than or equal to the standard deviation reference threshold value, generating a secondary early warning signal, transmitting the signal to a prompt module, and sending out a secondary early warning prompt through the prompt module;
if the precision index average value is smaller than the precision index reference threshold value and the precision index standard deviation is smaller than the standard deviation reference threshold value, generating a three-level early warning signal, transmitting the signal to the prompt module, and sending out early warning prompt without the prompt module.
4. An AIOT-based industrial solid waste treatment material management method realized by the AIOT-based industrial solid waste treatment material management system according to any one of claims 1 to 3, characterized by comprising the steps of:
acquiring a plurality of data information including physical property data information and communication property data information when a gas detector operates in an AIOT-based industrial solid waste treatment material management system, and processing the physical property data information and the communication property data information when the gas detector operates after acquisition;
comprehensively analyzing the processed physical property data information and communication property data information of the gas detector during operation to generate an accuracy index;
comparing and analyzing the precision index generated by the gas detector during operation with a preset precision index reference threshold value to generate a high hidden danger signal or a low hidden danger signal;
after the high hidden danger signal generated during the operation of the gas detector is obtained, the analysis set is established for the precision index generated during the subsequent operation of the gas detector for comprehensive analysis, the early warning signal is generated, and different early warning prompts are sent out to the early warning signal.
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